Abstract
Model comparison problems arise in many fields of science and engineering,
including signal processing. In these problems, we wish to quantify
how well each of a set of possible models describes a set of observations.
Many numerical techniques exist to perform model comparison, but
this paper focuses on nested sampling, which is a numerical integration
algorithm for evaluating probabilities of models. The original formulation
of nested sampling is a strictly sequential algorithm. Most modern
advances in computing are via parallel processing, however, and we
therefore present a novel method for parallelizing nested sampling.
This paper sets out the mathematical foundation for this parallelization,
as well as ideas for implementing it. Three examples demonstrate
the effectiveness of the present parallel technique in realistic
scientific and engineering data analysis problems.
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